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Record W4411697024 · doi:10.3390/met15070709

Influence of Friction-Stir-Processing Parameters on the Microstructure and Local Mechanical Properties of an Aluminium-6% Magnesium-H18 Alloy

2025· article· en· W4411697024 on OpenAlexaff
Samir Mourad Chentouf, Philippe Grandmont, Mohammad Saadati, Nora Amimer, Mohammad Jahazi

Bibliographic record

VenueMetals · 2025
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsPolytechnique MontréalÉcole de Technologie Supérieure
Fundersnot available
KeywordsMicrostructureAluminiumMagnesiumMaterials scienceMetallurgyAlloyMagnesium alloy6063 aluminium alloy

Abstract

fetched live from OpenAlex

One major challenge of friction stir processing (FSP) is its sensitivity to parameters like advancing and rotational speeds. This study examined the effect of tool travel speed on the microstructural evolution and mechanical properties of a new-generation Al-6Mg alloy. Optical and electron microscopy, EBSD, and shear-punch testing (SPT) were used. Two travel speeds, 50 and 120 mm/min, revealed significant differences in microstructure and properties at ambient temperature. EBSD provided misorientation maps and boundary fraction data. Microstructure analysis showed continuous dynamic recrystallization in the nugget zone, with finer grains observed at the higher speed. Microhardness was greater on both sides at 120 mm/min. The TMAZ showed elongated grains at 120 mm/min, while recrystallized grains were more prominent at 50 mm/min. In the HAZ, partial recrystallization occurred at 120 mm/min, whereas extensive recrystallization was observed at 50 mm/min. The SPT results indicated variations in stiffness between advancing and retreating sides, especially 2 mm from the nugget center. At 10 and 20 mm from the center, higher stiffness and strength were recorded at 120 mm/min. This study established correlations between joint stiffness, grain misorientation, and travel speed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.208
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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